一、主 题：Deep Learning with Big Health Data for Early Cancer Detection and Prevention
二、主讲人：耶鲁大学 JUN DENG 教授
Cancer is a worldwide public health issue with an estimated 21.7 million new cases over the world by 2030. Although a tremendous amount of money and resource has been deployed in the cancer treatments over the past 50 years, the cancer mortality worldwide is still worrisomely high in some parts of the world. Besides the disparity in the resource, environment, diet, lifestyle and ethnicity, one of the major reasons for high cancer mortality is the failing in diagnosing cancers at early stages, missing the best window of opportunity for intervention and cure. Recently, at the advent of big health data, more and more machine learning strategies have been proposed for the benefits of cancer patients. In this talk, we will introduce the basics of machine learning with big health data, highlight a few novel deep learning approaches for personalized cancer risk prediction, and discuss some potential applications in the future.
Dr. Jun Deng is a Professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an American Board of Radiology (ABR) board certified medical physicist at Yale-New Haven Hospital. Dr. Deng received his B.S. in physics from Sichuan University in 1991, his Ph.D. in physics from University of Virginia in 1998, and finished his postdoctoral fellowship at Department of Radiation Oncology of Stanford University School of Medicine in 2001. Since 2001 Dr. Deng has joined Yale University Department of Therapeutic Radiology as a faculty physicist. Dr. Deng has been serving on the study sections of National Institutes of Health (NIH), Department of Defense (DOD), American Association of Physicists in Medicine (AAPM), American Society for Radiation Oncology (ASTRO), and Radiological Society of North America (RSNA) since 2005, as scientific reviewer for European Science Foundation and Dutch Cancer Society since 2015, and on the Editorial Board of 15 peer-reviewed journals. Dr. Deng has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. At Yale, Dr. Deng’s research has been focused on big data, machine learning, artificial intelligence-assisted early cancer detection and prevention, medical imaging, organ dose and cancer risk assessment in radiation oncology and biomedical engineering. Currently, with NIH R01 grant support, Dr. Deng is leading his team in developing personalized cancer prediction models for specific tumors with deep learning algorithms. In addition, Dr. Deng is co-editing a book entitled ‘Big Data in Radiation Oncology’ to be published by Taylor & Francis/CRC Press in 2017, and a research topic entitled ‘Machine Learning with Radiation Oncology Big Data’ to be published by Frontiers in Oncology in 2018.
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